One goal in biochemical research is to develop correlations between the presence, absence, concentration, conversion rates, or transport rates of certain molecules within cells, tissues and particular cell or tissue states (e.g., disease states, particular developmental stages, states resulting from exposure to certain environmental stimuli and states associated with therapeutic treatments). Such correlations have the potential to provide significant insight into the mechanism of disease, cellular development and differentiation, as well as in the identification of new therapeutics, drug targets and/or disease markers.
Genomics based studies are an example of one type of approach taken in such investigations. Typically, functional genomics focuses on the change in mRNA levels as being indicative of a cellular response to a particular condition or state. Recent research, however, has demonstrated that often there is a poor correlation between gene expression as measured by mRNA levels and active gene product formed (i.e., protein encoded by the mRNA). This finding is not particularly surprising since many factors—including differences in translational efficiency, turnover rates, extracellular expression or compartmentalization, and post-translational modification affect protein levels independently of transcriptional controls.
Another approach is proteomics which, as the term implies, focuses on the proteins present in various cellular states. The rationale for conducting proteomics investigations is based in part upon the view that certain aspects of cellular biology can be better understood by taking inventory of protein levels rather than nucleic acids levels, particularly given the findings just described that suggest that protein activity often hinges on factors other than the concentration of mRNA encoding the protein.
Instead of focusing exclusively on either nucleic acids or proteins, the current invention takes a different approach and examines the metabolites present in a cell formed through cellular metabolism. Such an approach is termed metomics. More specifically, metomics refers to the study of metabolic fluxes and changes in these fluxes as a function of the physiological state of an organism (or population of cells or tissue). Metomics studies can involve, for example, identifying specific metabolic patterns that cause or result from changes in the physiological state of an organism or cell population. Metomics studies can be correlated to changes in protein and mRNA expression patterns also resulting from changes in the physiological state of an organism or cell population.
Metabolism consists of a complex network of catabolic (energy and precursor producing) and anabolic (biosynthetic) enzymatic pathways that together support the maintenance and growth of the cell. The flow of chemicals through this network of enzymatic reactions ovaries with the cell cycle; (Ingraham, J. L., et al., Growth of the Bacterial Cell, Sinauer Associates, Sunderland, Mass., (1983)) diet, availability of extracellular nutrients, and exposure to cellular stresses (e.g., chemical and biochemical toxins or infectious agents). The major metabolic pathways and factors in their regulation are discussed in any general biochemical text book including, for example, Voet, D. and Voet, J. G., Biochemistry, John Wiley & Sons, New York (1990); Stryer, L., Biochemistry, 2nd ed., W.H. Freeman and Company, San Francisco (1981); and White, A., et al., Principles of Biochemistry, 6th ed., McGraw-Hill Book Company (1978), each of which is incorporated by reference in its entirety.
Because metabolism must be capable of adapting to varying conditions and stimuli, cells have a variety of mechanisms at their disposal to regulate metabolism. For example, certain regulatory mechanisms control the rate at which metabolites enter a cell. Since very few substances are capable of diffusing across a cellular membrane, such regulation typically occurs via one of the active or passive transport mechanisms of a cell.
In addition to transport control, a number of different mechanisms can function to regulate the activity of an enzyme that is part of a metabolic pathway. For example, a product produced by the enzyme can act via feedback inhibition to regulate the activity of the enzyme. Enzymes can also be regulated by ligands that bind at allosteric sites (i.e., sites other than the active site of the enzyme). It has been suggested that allosteric regulation is important in quick time responses (times less than that required for the induction and synthesis of new proteins, <10 min), as well as in the modulation of enzyme activity to changes in background requirements (feed-back control) (Chock, P. B., et al., Current Topics in Cellular Regulation., 27:3 (1985); Koshland, D. E., et al., Science, 217:220 (1982); Stadtman, E. R. and Chock, P. B., Current Topics in Cellular Regulation, 13:53 (1978)). Allosteric regulation is the primary method used by bacteria to sense their environment, both by activity modulation of already synthesized proteins and by eliciting new protein synthesis via control of RNA polymerase promoter and repressor proteins (Monod, J., et al., J. Mol. Biol., 6:306 (1963)). Allosteric regulation can be associated with multimeric proteins (several subunits working in a concerted fashion) and/or within regulatory cascades in order to: (1) provide more sites for different regulatory ligands to affect activity, (2) amplify the rate of response, (3) amplify the magnitude of response, and/or (4) amplify the sensitivity of response (Chock, P. B., et al., Current Topics in Cellular Regulation., 27:3 (1985); Koshland, D. E., et al., Science, 217:220 (1982); Stadtman, E. R. and Chock, P. B., Current Topics in Cellular Regulation, 13:53 (1978)).
Expression regulation constitutes another metabolic regulatory mechanism. Concerted sets of genes, encoding small numbers of proteins, are often organized under the same transcriptional control sequence called an operon. However, where the necessary adaptive changes entail the induction of large numbers of proteins, many such operons can be linked in regulons. For example, in E. coli the following stimuli induce the number of proteins indicated in parentheses: (a) heat shock (17 proteins), (b) nitrogen starvation (≧5 proteins), (c) phosphate starvation (≧82 proteins), (d) osmotic stress (≧12 proteins), and (e) SOS response (17 proteins) (see, Neidhardt, F. C., in: Escherichia coli and Salmonella typhimurium: cellular and molecular biology, F. C. Neidhardt et al. (eds.), pg. 3, Amer Soc Microbiology, Washington, D.C., (1987); Neidhardt, F. C. and Van Bogelen, R. A., in: Escherichia coli and Salmonella typhimurium Cellular and Molecular Biology., F. C. Neidhardt (ed.)., pg 1334, American Society of Microbiology, Washington, D. C., (1987); Magasanik, B. and Neidhardt, F. C., in Escherichia coli and Salmonella typhimurium Cellular and Molecular Biology., F. C. Neidhardt (ed.), pg 1318, American Society of Microbiology, Washington, D.C., (1987); (VanBogelen, R. A., et al., Electrophoresis, 11:1131 (1990)); Wanner, B. L., in: Escherichia coli and Salmonella typhimurium Cellular and Molecular Biology, F. C. Neidhardt (ed.), pg 1326, American Society of Microbiology, Washington, D.C., (1987)); (Christman, M. F. et. al, Cell, 14:753 (1985); and Walker, G. C., in Escherichia coli and Salmonella typhimurium Cellular and Molecular Biology, F. C. Neidhardt (ed.), pg 1346, American Society of Microbiology, Washington, D.C., (1987)). Thus, regulons enable cells to regulate genes that need to respond occasionally in a concerted fashion to a particular stimulus, but that at other times need to be independently responsive to individual controls (Neidhardt, F. C., in: Escherichia coli and Salmonella typhimurium: cellular and molecular biology, F. C. Neidhardt et al. (eds.), pg. 3, Amer Soc Microbiology, Washington, D.C., (1987)).
Degradation is another regulatory mechanism for controlling metabolism. Most proteins are very stable, at least under conditions of balanced growth, probably because the cell pays such a high price to make them. However, several researchers have observed a limited class of cellular proteins (10 to 30% of the total protein present during exponential growth in bacteria) that is unstable (exhibit half-lives of 60 min or less). Proteins within the class appear to be turned over quickly within 10 hours of any growth down shift, and during exponential growth (Nath K. and Koch. A. L., J. Biol. Chem., 246:6956 (1971); St. John, A. C. and Goldberg, A. L., J. Bacteriol., 143:1223 (1980)). At least some of these labile proteins, during energy and nutrient down-shifts, are proteins of the protein synthesizing system (e.g., ribosomal proteins) (Davis, B. D., et al., J. Bacteriol., 166:439 (1986)); Ingraham, J. L., et al., Growth of the Bacterial Cell, Sinauer Associates, Sunderland, Mass., (1983); Maruyama, H. B. and Okamura, S., J. Bacteriol., 110:442 (1972)). This conclusion is drawn from the observations that the apparent rate of protein synthesis per unit of protein synthesizing proteins decreases at low growth rates, but the time required for the initial synthesis of inducible enzymes remains constant at all growth rates (Ingraham, J. L., et al., Growth of the Bacterial Cell, Sinauer Associates, Sunderland, Mass. (1983)).
Given the interrelatedness between different cell states and metabolism and the fact that the focus of metomics differs from genomics and proteomics, the present invention utilizes metomic studies to gain new insight into the correlation between cellular states and the biomolecules within the cell.